ArcDrain: A GIS Add-In for Automated Determination of Surface Runoff in Urban Catchments

ABSTRACT: Surface runoff determination in urban areas is crucial to facilitate ex ante water planning, especially in the context of climate and land cover changes, which are increasing the frequency of floods, due to a combination of violent storms and increased imperviousness. To this end, the spatial identification of urban areas prone to runoff accumulation is essential, to guarantee effective water management in the future. Under these premises, this work sought to produce a tool for automated determination of urban surface runoff using a geographic information systems (GIS). This tool, which was designed as an ArcGIS add-in called ArcDrain, consists of the discretization of urban areas into subcatchments and the subsequent application of the rational method for runoff depth estimation. The formulation of this method directly depends on land cover type and soil permeability, thereby enabling the identification of areas with a low infiltration capacity. ArcDrain was tested using the city of Santander (northern Spain) as a case study. The results achieved demonstrated the accuracy of the tool for detecting high runoff rates and how the inclusion of mitigation measures in the form of sustainable drainage systems (SuDS) and green infrastructure (GI) can help reduce flood hazards in critical zonesThis research was funded by the Spanish Ministry of Science, Innovation, and Universities, with funds from the State General Budget (PGE) and the European Regional Development Fund (ERDF), grant number RTI2018-094217-B-C32 (MCIU/AEI/FEDER, UE)

Spatial Statistical Modeling of Rockfall Hazard in a Mountainous Road in Cantabria (Spain)

Rockfall events are one of the most frequent types of mass wasting in mountainous areas, causing service and traffic disruption, as well as infrastructure and human damage. Hence, having accurate tools to model these hazards becomes crucial to prevent fatalities, especially in a context of climate change whereby the effects of these phenomena might be exacerbated. Under this premise, this article concerned the development of a framework for assessing rockfall hazard in mountainous areas. First, a set of factors expected to favor rockfalls were processed and aggregated using spatial analysis tools, yielding a series of hazard maps with which to fit observed data through statistical modeling. The validation process was undertaken with the support of a database containing the number of rocks removed from a mountainous road section located in Cantabria, northern Spain. The results achieved, which demonstrated the accuracy of the proposed approach to reproduce rockfall hazard using frequency data, highlighted the primary role played by factors such as slope, runoff threshold, and precipitation to explain the occurrence of these events. The effects of climate change were considerably influenced by the fluctuations in the projections of precipitation, which limited the variations in the spatial distribution and magnitude of rockfall hazard.This work was supported in part by the Spanish Ministry of Science, Innovation, and Universities, in part by the State General Budget (PGE), and in part by the European Regional Development Fund (ERDF)under Grant RTI2018-094217-B-C32 (MCIU/AEI/FEDER, UE). The work of Alejandro Roldan-Valcarce was supported by the Spanish Ministry of Science, Innovation and Universities through a Researcher Formation Fellowship under Grant PRE2019-08945

Vulnerability to urban flooding assessed based on spatial demographic, socio-economic and infrastructure inequalities

Urban flooding is a priority in natural risk management and mitigation because it is the most frequent natural disaster in densely urbanised environments. This research explores flood vulnerability in cities by developing an index that can be easily implemented across the world. Our methodology is based on the arrangement of a series variables into three different classes (demography, socioeconomics and infrastructure) and the determination of their spatial variability through a Principal Component Analysis (PCA). We tested the proposed approach in the city of Santander (Spain) where a vulnerability index map was generated based on the combination of the proposed classes. The analysis show that we can reduce complexity from an initially identified 159 relevant variables to 16 representative and impactful variables in terms of spatial variance. Classification of the variables into three different classes made it possible to quantify the main causes of vulnerability to flooding across space. We produce a flood risk map by integrating our findings with a flood hazard map for the same area. This flood risk map gives urban planners detailed information about the most affected areas and allows them to design measures that mitigate the severity and effects of floods optimising available resources

Treatment with tocilizumab or corticosteroids for COVID-19 patients with hyperinflammatory state: a multicentre cohort study (SAM-COVID-19)

Objectives: The objective of this study was to estimate the association between tocilizumab or corticosteroids and the risk of intubation or death in patients with coronavirus disease 19 (COVID-19) with a hyperinflammatory state according to clinical and laboratory parameters. Methods: A cohort study was performed in 60 Spanish hospitals including 778 patients with COVID-19 and clinical and laboratory data indicative of a hyperinflammatory state. Treatment was mainly with tocilizumab, an intermediate-high dose of corticosteroids (IHDC), a pulse dose of corticosteroids (PDC), combination therapy, or no treatment. Primary outcome was intubation or death; follow-up was 21 days. Propensity score-adjusted estimations using Cox regression (logistic regression if needed) were calculated. Propensity scores were used as confounders, matching variables and for the inverse probability of treatment weights (IPTWs). Results: In all, 88, 117, 78 and 151 patients treated with tocilizumab, IHDC, PDC, and combination therapy, respectively, were compared with 344 untreated patients. The primary endpoint occurred in 10 (11.4%), 27 (23.1%), 12 (15.4%), 40 (25.6%) and 69 (21.1%), respectively. The IPTW-based hazard ratios (odds ratio for combination therapy) for the primary endpoint were 0.32 (95%CI 0.22-0.47; p < 0.001) for tocilizumab, 0.82 (0.71-1.30; p 0.82) for IHDC, 0.61 (0.43-0.86; p 0.006) for PDC, and 1.17 (0.86-1.58; p 0.30) for combination therapy. Other applications of the propensity score provided similar results, but were not significant for PDC. Tocilizumab was also associated with lower hazard of death alone in IPTW analysis (0.07; 0.02-0.17; p < 0.001). Conclusions: Tocilizumab might be useful in COVID-19 patients with a hyperinflammatory state and should be prioritized for randomized trials in this situatio

ArcUHI: A GIS add-in for automated modelling of the Urban Heat Island effect through machine learning

Increased urbanisation is boosting the intensity and frequency of the Urban Heat Island (UHI) effect in highly developed cities. The advances in satellite measurement are facilitating the analysis of this phenomenon using Land Surface Temperature (LST) as an indicator of the Surface UHI (SUHI). Due to the spatial implications of using remote sensing data, this research developed ArcUHI, a Geographic Information System (GIS) add-in for modelling SUHI. The tool was designed in ArcGIS, which was bound with R to run machine learning algorithms in the background. ArcUHI was tested using the metropolitan area of Madrid (Spain) in 2006, 2012 and 2018 as a case study. The add-in was found to predict observed LST with high accuracy, especially when using Random Forest Regression (RFR). Building height and albedo were identified as the main drivers of SUHI, whose magnitude and extension increased with time. In view of these results, strategic roof and wall greening was suggested as a measure to mitigate the street canyon effect entailed by buildings and offset the heat retention capacity of built-up surfaces.This research was funded by the Spanish Ministry of Science, Innovation, and Universities with funds from the State General Budget (PGE) and the European Regional Development Fund (ERDF), grant number RTI2018-094217-B-C32 (MCIU/AEI/FEDER, UE). Alejandro Roldán-Valcarce thanks the Spanish Ministry of Science, Innovation and Universities for funding his investigations at the University of Cantabria through a Researcher Formation Fellowship, grant number PRE2019-089450

Vulnerability to urban flooding assessed based on spatial demographic, socio-economic and infrastructure inequalities

Urban flooding is a priority in natural risk management and mitigation because it is the most frequent natural disaster in densely urbanised environments. This research explores flood vulnerability in cities by developing an index that can be easily implemented across the world. Our methodology is based on the arrangement of a series variables into three different classes (demography, socioeconomics and infrastructure) and the determination of their spatial variability through a Principal Component Analysis (PCA). We tested the proposed approach in the city of Santander (Spain) where a vulnerability index map was generated based on the combination of the proposed classes. The analysis show that we can reduce complexity from an initially identified 159 relevant variables to 16 representative and impactful variables in terms of spatial variance. Classification of the variables into three different classes made it possible to quantify the main causes of vulnerability to flooding across space. We produce a flood risk map by integrating our findings with a flood hazard map for the same area. This flood risk map gives urban planners detailed information about the most affected areas and allows them to design measures that mitigate the severity and effects of floods optimising available resources.This research was funded by the Conselleria for Innovation, Universities, Science and Digital Society of the Generalitat Valenciana through the project CIGE/2021/079, as well as by the Spanish Ministry of Science and Innovation (MCIN) and the Spanish State Research Agency (AEI) through the project PID2021-122946OB-C33 financed by MCIN/AEI/10.13039/501,100,011. Alejandro Roldán-Valcarce thanks the Spanish Ministry of Science, Innovationfor funding his investigations at the University of Cantabria through a Researcher Formation Fellowship, grant number PRE2019-089,450